A robust task scheduler server built with Model Context Protocol (MCP) for scheduling and managing various types of automated tasks
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-scheduler-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# MCP Scheduler A robust task scheduler server built with Model Context Protocol (MCP) for scheduling and managing various types of automated tasks.  ## Overview MCP Scheduler is a versatile task automation system that allows you to schedule and run different types of tasks: - **Shell Commands**: Execute system commands on a schedule - **API Calls**: Make HTTP requests to external services - **AI Tasks**: Generate content through OpenAI models - **Reminders**: Display desktop notifications with sound The scheduler uses cron expressions for flexible timing and provides a complete history of task executions. It's built on the Model Context Protocol (MCP), making it easy to integrate with AI assistants and other MCP-compatible clients. ## Features - **Multiple Task Types**: Support for shell commands, API calls, AI content generation, and desktop notifications - **Cron Scheduling**: Familiar cron syntax for precise scheduling control - **Run Once or Recurring**: Option to run tasks just once or repeatedly on schedule - **Execution History**: Track successful and failed task executions - **Cross-Platform**: Works on Windows, macOS, and Linux - **Interactive Notifications**: Desktop alerts with sound for reminder tasks - **MCP Integration**: Seamless connection with AI assistants and tools - **Robust Error Handling**: Comprehensive logging and error recovery ## Installation ### Prerequisites - Python 3.10 or higher - [uv](https://astral.sh/uv) (recommended package manager) ### Installing uv (recommended) ```bash # For Mac/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # For Windows (PowerShell) powershell -c "irm https://astral.sh/uv/install.ps1 | iex" ``` After installing uv, restart your terminal to ensure the command is available. ### Project Setup ```bash # Clone the repository git clone https://github.com/phialsbasement/mcp-scheduler.git cd mcp-scheduler # Create and activate a vi…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.